Wavelet integrated attention network with multi-resolution frequency learning for mixed-type wafer defect recognition

• Published in: Engineering applications of artificial intelligence Vol. 121; p. 105975
• Main Authors: Wei, Yuxiang, Wang, Huan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2023
• Subjects: Convolutional neural network; Semiconductor manufacturing; Wafer defect recognition; Wavelet transform; Wavelet transform; Semiconductor manufacturing; Wafer defect recognition; Convolutional neural network
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2023.105975

Wafer defect recognition has been an important measure in developing the manufacturing process. In real-life manufacturing, however, defects can be complicated, and their wafer maps are often accompanied by noise. This encourages us to build a noise-robust framework with outstanding performance and sound interpretability on defect recognition. Therefore, this paper chooses discrete wavelet transform for its clear physical meaning and ability of frequency learning. Moreover, it outperforms traditional down-sampling operations in the preservation of fringe information. Based on this, we propose a multiresolution wavelet integrated attention network (MRWA-Net). Specifically, we design a learnable discrete wavelet transform layer (DWT-Layer), which expands convolutional neural network’s (CNN’s) feature learning space to the wavelet domain. This helps the framework procure hidden information from different frequency components and their location information. Furthermore, we utilize different levels of wavelet transform to interpret the images with different resolutions, thus learning features from different perspectives. Additionally, we insert a frequency-location attention module (FLA) to select the useful frequency-location information captured by DWT-Layer. The proposed approach is evaluated on a dataset with 38015 subjects and 38 types of defects and reaches 98.84% accuracy. To demonstrate the noise-robustness of our framework, we further compare it with other state-of-the-art methods on wafer maps with different ratios of additional noise. The results show that our framework excels other methods under all noise ratios and exhibits more notable excellence on data accompanied by a higher ratio of noise. Finally, we present visualizing analysis to demonstrate that the proposed DWT-Layer can learn from different frequency bands and retrieve information with multiple resolutions.